1] Department of Medicine, Division of Gastroenterology, Stanford University School of Medicine, Stanford, California, USA. [2] Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.

2

Program in Computational and Systems Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California, USA.

3

Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.

4

1] Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA. [2] Program in Computational and Systems Immunology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California, USA. [3] Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA.

Erratum in

Abstract

Although each T lymphocyte expresses a T-cell receptor (TCR) that recognizes cognate antigen and controls T-cell activation, different T cells bearing the same TCR can be functionally distinct. Each TCR is a heterodimer, and both α- and β-chains contribute to determining TCR antigen specificity. Here we present a methodology enabling integration of information about TCR specificity with information about T cell function. This method involves sequencing of TCRα and TCRβ genes, and amplifying functional genes characteristic of different T cell subsets, in single T cells. Because this approach retains information about individual TCRα-TCRβ pairs, TCRs of interest can be expressed and used in functional studies, for antigen discovery, or in therapeutic applications. We apply this approach to study the clonal ancestry and differentiation of T lymphocytes infiltrating a human colorectal carcinoma.

Strategy for single-cell TCR sequencing and phenotyping, and determination of TCR-sequencing efficiency. (a) Strategy for simultaneous TCR-sequence determination and phenotyping of single, sorted T cells. Single T cells were sorted into 96-well plates. The initial RT-PCR reaction (reaction 1) uses 76 TCR primers and 34 phenotyping primers. An aliquot of the product of reaction 1 is used for two separate second nested PCR reactions (reaction 2), one for TCR sequencing and one for phenotyping. Using an aliquot of reaction 2 product as a template, a third PCR reaction is performed that incorporates individual barcodes into each well and enables subsequent sequencing using the Illumina MiSeq platform. For TCR sequencing, the third reaction can be split into separate TCRα and TCRβ reactions (for optimal efficiency), or the two TCR chains can be included in a single reaction. The products of reaction 3 are then combined and sequenced using the Illumina MiSeq platform. (b,c) Accuracy and efficiency of TCR sequencing using this method. (b) Strategy used to validate TCR sequencing. Into each 96-well test plate, individual human peripheral blood T cells were sorted into 80 wells (gray). Single Jurkat T cells were sorted into eight other wells (red), and the remaining eight wells (black) were left empty (blank). For sequencing of these test plates, reaction 3 was initially performed separately for TCRα and TCRβ (split). It was also repeated with TCRα and TCRβ amplified together in the same reaction (combined). (c) Efficiency of TCRα and TCRβ sequencing in split or combined formats. Plate 1 contained 80 single CD45RA+CD4+TCRαβ+ T cells, and plate 2 contained 80 single CD4+ or CD8+ TCRαβ+ T cells sorted from periphera blood of the same healthy human donor. Identical Jurkat sequences were obtained from all Jurkat wells. No sequences were obtained from any empty wells.

Accuracy of phenotypic analysis. (a-f) Peripheral blood T cells were stimulated for 3 h with PMA and ionomycin, and expression of the ndicated cytokines was analyzed by cytokine secretion assays that do not require cell fixation: IFN-γ(a), TNF-α(b), IL-2 (c), IL-10 (d), IL-13 (e), IL-17 (f). Sixty single CD45RO+CD4+ T cells that were clearly positive for the indicated cytokine protein and 36 single CD45RO+CD4+ T cells that were clearly negative for the indicated cytokine protein were sorted, and expression of the same cytokine genes were measured by the method depicted in . Seventeen independent phenotypic parameters were assayed in single, sorted cells, and the phenotypic parameter on which cells were sorted is indicated in red. Heatmaps indicate read count of each parameter (x axis) within each particular well (y axis). Scale indicates number of reads obtained from a given well for the indicated parameter. Wells indicated in blue did not display any reads that reached threshold. (g) Unstimulated CD4+ T cells were sorted based upon CD25 expression to validate phenotypic analysis for FOXP3. Sixty single CD4+ T cells with high CD25 expression and 36 single CD4+ T cells that were negative for CD25 expression by flow cytometry were sorted and assayed as in a-f. (h) Expression of CD25 and FOXP3 protein was measured by flow cytometry. Cells from the same donor were fixed and stained with anti-CD25 and anti-FOXP3 antibodies. Histograms on right depict FOXP3 expression in gated CD25high and CD25− populations.

TCR sequencing and phenotypic analysis of single human TILs. (a) T cells were sorted and analyzed using the procedure from . PCA to depict phenotypic diversity of PMA- plus ionomycin-stimulated CD4+ T cells from tumor (blue) and adjacent colon (red) of a single patient, and from peripheral blood of another healthy donor (black). PCA parameter loadings are shown in . Each dot represents a single T cell. (b) Top two panels: 17-parameter (parameters listed on x axis) phenotypic analysis of stimulated CD4+ T cells from tumor (top) and colon (bottom) of a single patient. Individual T cells are grouped by TCR sequence; each color on the bar above the heat maps represents a distinct TCR sequence. (c) Hierarchical clustering of different cells by phenotype, with expanded (yellow) and unexpanded (black) T-cell clones (read out by TCR sequence) indicated in the horizontal bar above the heat map.